While high capex is often seen as a negative, for giants like Alphabet and Microsoft, it functions as a powerful moat in the AI race. The sheer scale of spending—tens of billions annually—is something most companies cannot afford, effectively limiting the field of viable competitors.

Early tech giants like Google and AWS built monopolies because their potential wasn't widely understood, allowing them to grow without intense competition. In contrast, because everyone knows AI will be massive, the resulting competition and capital influx make it difficult for any single player to establish a monopoly.

The historical advantage of being first to market has evaporated. It once took years for large companies to clone a successful startup, but AI development tools now enable clones to be built in weeks. This accelerates commoditization, meaning a company's competitive edge is now measured in months, not years, demanding a much faster pace of innovation.

Building software traditionally required minimal capital. However, advanced AI development introduces high compute costs, with users reporting spending hundreds on a single project. This trend could re-erect financial barriers to entry in software, making it a capital-intensive endeavor similar to hardware.

Unlike traditional SaaS, achieving product-market fit in AI is not enough for survival. The high and variable costs of model inference mean that as usage grows, companies can scale directly into unprofitability. This makes developing cost-efficient infrastructure a critical moat and survival strategy, not just an optimization.

Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.

An enterprise CIO confirms that once a company invests time training a generative AI solution, the cost to switch vendors becomes prohibitive. This means early-stage AI startups can build a powerful moat simply by being the first vendor to get implemented and trained.

A unique dynamic in the AI era is that product-led traction can be so explosive that it surpasses a startup's capacity to hire. This creates a situation of forced capital efficiency where companies generate significant revenue before they can even build out large teams to spend it.

As AI's bottleneck shifts from compute to data, the key advantage becomes low-cost data collection. Industrial incumbents have a built-in moat by sourcing messy, multimodal data from existing operations—a feat startups cannot replicate without paying a steep marginal cost for each data point.